Probabilistic boosting tree framework for learning discriminative models

Abstract

A probabilistic boosting tree framework for computing two-class and multi-class discriminative models is disclosed. In the learning stage, the probabilistic boosting tree (PBT) automatically constructs a tree in which each node combines a number of weak classifiers (e.g., evidence, knowledge) into a strong classifier or conditional posterior probability. The PBT approaches the target posterior distribution by data augmentation (e.g., tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. In the training stage, a tree is recursively constructed in which each tree node is a strong classifier. The input training set is divided into two new sets, left and right ones, according to the learned classifier. Each set is then used to train the left and right sub-trees recursively.

1. A method of classifying an image of objects into different image categories comprising the steps of:
 recursively constructing a probabilistic boosting tree in which each tree node is a strong classifier, a discriminative model being obtained at the top of the tree and each level of the tree comprising an augmented variable; 
 dividing an input training set into two new sets according to a learned classifier defined by an optimal feature for dividing the input training set; using the two new sets to train a left and right sub-trees recursively, wherein clustering is automatically formed in a hierarchical way; 
 calculating a probability that the image falls within a left sub-tree of the top of the tree and calculating a probability that the image falls within a right sub-tree of the top of the tree by recursively determining left and right sub-tree probabilities at each level of the tree; and 
 outputting the likelihood that the image matches the left sub-tree of the top of the tree and the likelihood that the image matches the right sub-tree of the top of the tree based on the calculated probabilities, 
 wherein each of the method steps are performed within an image processing apparatus or device. 
 
     
     
       2. The method of  claim 1  wherein the probabilistic tree solves a two class problem. 
     
     
       3. The method of  claim 1  wherein the step of outputting an appropriate number of classifications comprises a positive class and a negative class. 
     
     
       4. The method of  claim 1  wherein the probabilistic tree solves a multi-class problem. 
     
     
       5. The method of  claim 4  wherein the step of outputting an appropriate number of classifications comprises multiple categories of a predetermined number. 
     
     
       6. The method of  claim 1  wherein the object is an anatomical structure. 
     
     
       7. The method of  claim 6  wherein the anatomical structure is a left ventricle. 
     
     
       8. The method of  claim 6  wherein the anatomical structure is a fetus head. 
     
     
       9. The method of  claim 6  wherein the anatomical structure is a fetus abdomen. 
     
     
       10. The method of  claim 6  wherein the anatomical structure is a fetus femur. 
     
     
       11. The method of  claim 6  wherein the anatomical structure is a face. 
     
     
       12. The method of  claim 6  wherein the anatomical structure is a rectal tube.